For a class of nonlinear systems with non-parametric uncertainties, we present a robust iterative learning control algorithm, in which the iterative initial value can be arbitrary, relaxing the initial conditions required in conventional methods. The learning controller is designed based on the Lyapunov-like synthesis, compensating uncertainties by robust limited-magnitude learning mechanism, and the error can follow its desired trajectory accurately in the entire time interval. The desired error trajectory with attenuation traits is predetermined by the initial error value at the beginning of the iteration. The error converges to the neighborhood of the origin after the predetermined time, and the radius of the neighborhood can be as small as required. The effectiveness of the proposed method is proven by theoretical analysis and verification results.%针对一类含非参数不确定性的非线性系统,提出一种鲁棒迭代学习控制算法,该算法放宽了常规迭代学习控制方法的初始定位条件,迭代初值可任意取值.基于类Lyapunov方法设计误差轨迹跟踪控制器,通过鲁棒限幅学习机制对不确定性进行估计和补偿,能够在整个作业区间上实现误差对给定期望误差轨迹的精确跟踪,期望误差轨迹根据迭代起始时刻的误差值设置.利用期望误差轨迹的衰减性状,可使系统误差在预设的时间点后收敛于原点的邻域内,邻域半径的大小可根据需要任意设置.理论分析和仿真结果表明了控制方法的有效性.
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